Ma: Ma et al. [1]} proposed a learning-based no-reference image quality assessment. It is designed to focus on SR images, while other learning-based methods are applied to images degraded by noise, compression, or fast fading rather than SR. It learns from perceptual scores based on human subject studies involving a large number of SR images. And then it quantifies the SR artifacts through three types of statistical properties, i.e., local/global frequency variations and spatial discontinuity. Then these features are modeled by three independent learnable regression forests respectively to fit the perceptual scores of SR images, \(\hat{y}_n (n=1,2,3)\) . The final predicted quality score is \(\hat{y}=\sum _n \lambda _n\cdot \hat{y}_n\) , and the weight \(\lambda \)  is learned by minimizing \(\lambda ^*=\mathop {\arg \min }_{\lambda } (\sum _n \lambda _n\cdot \hat{y}_n-y)^2\) .
